1. Field of the Invention
The present invention relates to an abnormal (unusual) action detector and an abnormal action detecting method for capturing moving images to automatically detect actions different from usual.
2. Description of the Related Art
Currently, camera-based monitoring systems are often used in video monitoring in the field of security, an elderly care monitoring system, and the like. However, manual detection of abnormal actions from moving images requires much labor, and a computer substituted for the manual operation would lead to a significant reduction in labor. Also, in the elderly care, an automatic alarm system for accesses and the like, if any, would reduce a burden on care personnel, so that camera-based monitoring systems are required for informing abnormal actions and the like.
Thus, abnormal actions must be recognized from moving images to extract action features for an object. Studies on abnormal action recognition include, among others, Laid-open Japanese Patent Application No. 2006-079272 (Patent Document 1), filed by one of the inventors and one other, which discloses a technology for performing the abnormal action recognition using cubic higher-order local auto-correlation features (hereinafter called “CHLAC” feature data as well).
The conventional abnormal action recognition described above employs the cubic higher-order local auto-correlation features extracted from an entire screen, and the CHLAC data do not depend on the position, time and the like of the object and have values invariable in position, as action features. Taking advantage of the nature of additivity that when there are a plurality of objects in a cube, an overall feature value is the sum of individual feature values of the respective objects, normal actions available in abundance as normal data are statistically learned as a (linear) subspace, and abnormal actions are detected as deviations therefrom. In this way, when there are a plurality of persons, for example, on a screen, an abnormal action of even one person can be advantageously detected at high speeds without extraction or tracking of the individual persons.
However, the conventional abnormal action recognition has a problem of the inability to identify the position of an object which presents an abnormal action due to the position invariance of CHLAC data extracted from an entire screen. The conventional abnormal action recognition also has a problem of a lower detection accuracy when there are a plurality of types of objects per se, or abnormal actions.